Adaptation transculturelle d’un outil de mesure des opinions et croyances des infirmières en regard du développement professionnel continu : version canadienne-française du Q-PDN
Bibliographic record
Abstract
Introduction: The continuing professional development of nurses remains a key issue within the health context. Context: The Questionnaire - Professional Development Nurses (Q-PDN) was developed to understand the opinions and beliefs of nurses regarding continuing professional development is essential to improving the quality of care and optimizing job satisfaction. With this in mind, researchers from the Netherlands developed the Questionnaire – Professional Development Nurses (Q-PDN). Objectives: Adapt the Q-PDN to a French-Canadian context according to recognized guidelines. Method: The six stages of the Beaton et al. (2000) cross-cultural adaptation process were followed to produce a tool adapted to the French-Canadian context. Results and discussion: This study describes the different steps necessary for the cross-cultural adaptation of the Q-PDN tool to the French-Canadian context. Following the adaptation, minor content adjustments were made to the original version. General comments have highlighted the clarity and simplicity of the questionnaire. Conclusion: This first step in the cross-cultural French-Canadian adaptation of Q-PDN has enabled the development of a tool to identify gaps and priorities for action to promote the continuing professional development (CPD) of nurses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".